REAL-TIME DETECTION OF ANOMALOUS BEHAVIOR IN CCTV VIDEOS USING ADVANCED MACHINE LEARNING TECHNIQUES
International Research Journal of Modernization in Engineering Technology and Science · 2023
The detection of suspicious activities in public areas is crucial for maintaining security and preventing criminal incidents such as terrorism, theft, and other unlawful acts.This study presents an original approach for realtime identification of suspicious human activity in live CCTV footage using neural networks.Our method focuses on predicting the placement of body parts or joints from images or videos, enabling the tracking and differentiation of normal and abnormal movements.Emphasizing public spaces like bus stations, railway stations, airports, banks, shopping malls, schools, colleges, parking lots, and roads, where intelligent video surveillance is essential, our approach incorporates state-of-the-art deep learning techniques for object detection and tracking.Through comprehensive evaluation on large-scale video datasets, we demonstrate that our proposed method surpasses existing approaches in terms of accuracy and efficiency, providing valuable insights for enhancing public safety and security.By adopting our novel machine learning-based approach, stakeholders and security personnel can effectively identify and respond to suspicious activities, mitigating potential risks and strengthening security measures in public environments..